The search for new treatments for infectious diseases gets a lot of attention. But to treat something, we first need to know what we’re dealing with. That’s not always easy. The backbone of diagnosis is still built from old methods that include growing mystery germs in lab cultures, or checking how they react to specific chemicals. These techniques require special training and can be time-consuming. Unlike medical dramas, where diseases can be diagnosed between quips, the real-life work can take days.

Amy Barczak from Massachussetts General Hospital is developing a diagnostic technique based on RNA, a molecule that is closely related to DNA. Her method can detect a wide range of infections microbes (‘pathogens’), from bacteria to viruses to parasites. At the same time, it can tell if they are resistant to drugs. Barczak has now published an early “proof-of-principle” study showing that her method has potential, but she says that “considerable additional work will be required” to create a test for doctors to use.

It seems odd that diagnosis should be a problem for the age of modern genetics. Surely you could just sequence the DNA of whatever it is that’s causing an illness? That’s true, in principle. In practice, you need to know the genome of the pathogen in question, and you need to boost the amount of DNA in your sample. It’s even harder to scan antibiotic resistance, because there are hundreds of ways in which pathogens can tweak their genes (or gain new ones) that make them invulnerable. We know of only a fraction of them.

RNA offers an easier path. When genes are ‘switched on’, the information encoded within their DNA is transcribed into equivalent molecules of RNA. If a pathogen has DNA sequences that reveal its identity, it also has corresponding telltale RNA sequences. And of the two molecules, RNA is far more abundant. You can measure it straight from a patient’s sample, without needing to purify or amplify it.

Barczak first looked for RNA sequences that give away the presence of different bacteria, include E.coli, Pseudomonas aeruginosa, Staphylococcus auerus (the one behind MRSA), and Mycobacteria tuberculosis (which causes tuberculosis). She built glowing ‘probe’ molecules designed to recognise pieces of RNA that are always the same within a species, but different across them.

In laboratory cultures, the probes could accurately distinguish the various bacteria, right down to separate but closely related species. They were also effective at detecting: Candida albicans, the fungus that causes thrush; different life stages of the malaria parasite; and several viruses, including HIV, influenza and a herpes virus.

For now, if you wanted to check a patient for all of these germs, you’d need a range of different equipment and methods. Barczak’s technique could streamline that diversity. It’s still in a very early stage of development, but there’s the potential endpoint: imagine a grid of glowing probes, each responsive to the RNA of a different germ, or a tube of probes that each glowed a different colour. You could test a blood, spit or urine sample from your patient for many pathogens at once, just by following the light.

And RNA isn’t just a badge of identity. It’s a badge of activity too. Like the power light that flashes when your computer is on, RNA tells you that genes are being used. “It’s something dynamic, as compared to DNA which is just a static description of the cell,” says Deborah Hung, who led the study.

When bacteria are exposed to antibiotics, they respond within minutes. Those that are vulnerable switch on SOS genes to try and cope with the damage that the drugs inflict. Resistant bacteria aren’t that bothered; they have ways of neutralising or evicting the drugs, and less need for damage control. This means that there’s a immediate difference between vulnerable and resistant microbes, which you can measure long before it’s clear if the microbe will grow or die. And this difference doesn’t depend on how the microbes resist the drugs, just that they do.

Barczak identified RNA signatures that distinguish resistant bacteria from their more susceptible peers and checked for them, again with glowing probes. She managed to identify drug-resistant versions of E.coli, P.aeruginosa, and tuberculosis.

At the moment, it can take weeks or months to work out if a patient has a drug-resistant version of tuberculosis, which sometimes leads to long delays before people receive the most appropriate treatments. Barczak used her technique to identify M.tuberculosis that resists three different antibiotics in just 3 to 6 hours.

Barczak also showed that her glowing probes could separate drug-resistant bacteria from susceptible ones in actual blood samples that had been doped with bacteria, or urine specimens from patients suspected of having infections.

There’s still a lot of work to do. Barczak now needs to test her RNA technique at a larger scale to check how sensitive it is (how often it fails to spot a given pathogen), and how specific it is (how often it raises a false alarm). She also needs to identify clearer RNA signatures that distinguish drug-resistant pathogens.

Ellen Jo Baron, who studies the diagnosis of infectious diseases, says that the technique has “exciting possibilities”. For a start, it gives both the name and the amount of different pathogens, which would be helpful in cases where patients have mixed infections. The ability to quickly assess drug resistance would also be helpful. Many mutations could help tuberculosis to resist the drug pyrazinamide, and it would be impractical to scan for all of them within the microbe’s DNA. Barczaj’s RNA method would be perfect. “I urge them to work on it,” she says.

However, Baron thinks there are situations where the technique might struggle. For example, there are pairs of bacteria that are “genetically virtually impossible to distinguish”, such as E.coli and Shigella. Mixed infections might also pose a problem. Imagine that someone was infected with a drug-susceptible strain of staph that causes disease, and a drug-resistant strain that doesn’t. Could the RNA method work out where the resistance actually lies?

There’s also an issue of speed. Hung says the technique takes just 30 minutes to identify and describe fast-growing bacteria, and around 3 hours to pinpoint slower-growing species like M.tuberculosis. By comparison, most common methods take a day or two to identify fast-growing bacteria, and another day or two to work out if they’re resistant to drugs. For slow-growing bacteria like M.tuberculosis, it can take weeks rather than days.

However, Baron also notes that several other state-of-the-art tools could keep pace with the RNA technique. “Current high-tech methods have a maximum time-to-result of 6 hours but some PCR rapid tests [which are based on DNA – Ed] have 45 min detection times,” she says.

John, there are two problems. The first is that ribosomal RNA doesn’t go far enough “down” into the bacterial phylogeny. E. coli is not a single pathogen, but more than 7 or 8 different pathogens with quite different complements of virulence genes. Needless to say, ribosomal RNA just gets genus (Escherichia) and species (coli), which could apply to any of the pathogenic types or to E. coli that do not cause disease. rRNA cannot differentiate! But Bob Mandrell’s lab has just published a new method, quite complex and labour-intensive, using DNA sequences for a number of ribosomal proteins. Still, DNA hybridization, PCR, or the RNA methods above have been used and are very effective.
The second problem I have run up against is, in Canada and perhaps the U.S as well, all of these methods mess too much with the workflow of primary diagnostic labs, are too expensive, or require too much specialized expertise. So we still need better methods to detect enteroaggregative E. coli, for instance, which in several studies in England, Europe, and specific sites in the U.S. has been found to cause more human cases than all Salmonella serotypes put together. And we can’t do surveillance for it because we don’t have methods primary laboratories can use to find them!

Surely one of the main problems is that what makes RNA useful (gives you a picture of what is going on in the cell) is also it’s Achilles heel. What if the cells you are testing aren’t expressing the gene you are using in your assay at that particular time or in that particular situation?

Routine labs haven’t widely embraced using proteins for identification (beyond serology) because of fears that the cell isn’t always going to be making the protein when you are testing for it. Where proteins are used for microbial id (e.g. using MALDI-ToF), it is generally ribosomal proteins which are the target. If routine labs don’t want to use proteins they certainly aren’t going to be eager to use RNA which is much more troublesome.

Having said all that, I think that using it to probe drug resistance and the contribution of different bugs to mixed infections is really worthy of more research and I hope it happens. However, will still have to deal with many problems – the main one being is it expressing the same in vitro as it was in vivo (or when it is in a biofilm like many infections are)? Has the RNA in your sample stayed constant in the gap between sampling and testing?